Coupling Monte Carlo, Variational Implicit Solvation, and Binary Level-Set for Simulations of Biomolecular Binding

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

Background

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a progressive neurodegenerative disorder that gradually affects cognitive function and eventually causes death. Most approved drugs can only treat the disease alleviating the disease symptoms; therefore, there is a need to develop drugs that can treat this illness holistically. The medical community is searching for new drugs and new drug targets to cure this disease. In this study, QSAR, molecular docking evaluation, and ADMET/pharmacokinetics assessment were used as modeling methods to identify the compounds with outstanding physicochemical properties.

Results

The 37 MAO-B compounds were screened using the aforementioned methods and yielded a model with the following molecular properties: AATS1v, AATS3v, GATS4m, and GATS6e. Good statistical values were R2train = 0.69, R2adj = 0.63, R2pred = 0.57, LOF = 0.23, and RMSE = 0.38. The model was validated using an evaluation set that confirmed its robustness. The molecular docking was also utilized using crystal structure of human monoamine oxidase B in complex with chlorophenylchromone-carboxamide with ID code of 6FW0, and three compounds were identified with outstanding high binding affinity (13 = − 30.51 kcal mol−1, 31 = − 31.85 kcal mol−1, and 33 = − 33.70 kcal mol−1), and better than the Eldepryl (referenced) drug (− 11.40 kcal mol−1).

Conclusions

These three compounds (13, 31, and 33) were analyzed for ADMET/pharmacokinetics evaluation and found worthy of further analysis as promising drug candidates to cure AD and could also serve as a template to design several monoamine oxidase B inhibitors in the future to cure AD.

Engineering and Design of Programmable Genome Editors

Programmable genome editors are enzymes that can be targeted to a specific location in the genome for making site-specific alterations or deletions. The engineering, design, and development of sequence-specific editors has resulted in a dramatic increase in the precision of editing for nucleotide sequences. These editors can target specific locations in a genome, in vivo. The genome editors are being deployed for the development of genetically modified organisms for agriculture and industry, and for gene therapy of inherited human genetic disorders, cancer, and immunotherapy. Experimental and computational studies of structure, binding, activity, dynamics, and folding, reviewed here, have provided valuable insights that have the potential for increasing the functional efficiency of these gene/genome editors. Biochemical and biophysical studies of the specificities of natural and engineered genome editors reveal that increased binding affinity can be detrimental because of the increase of off-target effects and that the engineering and design of genome editors with higher specificity may require modulation and control of the conformational dynamics.

Prediction of multiple dry-wet transition pathways with a mesoscale variational approach

Water fluctuates in a hydrophobic confinement, forming multiple dry and wet hydration states through evaporation and condensation. Transitions between such states are critical to both thermodynamics and kinetics of solute molecular processes, such as protein folding and protein-ligand binding and unbinding. To efficiently predict such dry-wet transition paths, we develop a hybrid approach that combines a variational implicit solvation model, a generalized string method for minimum free-energy paths, and the level-set numerical implementation. This approach is applied to three molecular systems: two hydrophobic plates, a carbon nanotube, and a synthetic host molecule Cucurbit[7]uril. Without an explicit description of individual water molecules, our mesoscale approach effectively captures multiple dry and wet hydration states, multiple dry-wet transition paths, such as those geometrically symmetric and asymmetric paths, and transition states, providing activation energy barriers between different states. Further analysis shows that energy barriers depend on mesoscopic lengths, such as the separation distance between the two plates and the cross section diameter of the nanotube, and that the electrostatic interactions strongly influence the dry-wet transitions. With the inclusion of solute atomic motion, general collective variables as reaction coordinates, and the finite-temperature string method, together with an improved treatment of continuum electrostatics, our approach can be further developed to sample an ensemble of transition paths, providing more accurate predictions of the transition kinetics.